Defining sub-regions in locally sparsified compressive sensing MRI

Razzaq, Fuleah A., Mohamed, Shady, Bhatti, Asim and Nahavandi, Saeid 2013, Defining sub-regions in locally sparsified compressive sensing MRI, in BioMed 2013 : Proceedings of the 10th IASTED International Conference on Biomedical Engineering, ACTA Press, Calgary, Alb., pp. 360-367, doi: 10.2316/P.2013.791-045.

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Title Defining sub-regions in locally sparsified compressive sensing MRI
Author(s) Razzaq, Fuleah A.
Mohamed, ShadyORCID iD for Mohamed, Shady
Bhatti, AsimORCID iD for Bhatti, Asim
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Conference name IASTED Biomedical Engineering. Conference (10th : 2013 : Innsbruck, Austria)
Conference location Innsbruck, Austria
Conference dates 13-15 Feb. 2013
Title of proceedings BioMed 2013 : Proceedings of the 10th IASTED International Conference on Biomedical Engineering
Editor(s) Boccaccini, A.R.
Publication date 2013
Conference series IASTED Biomedical Engineering Conference
Start page 360
End page 367
Total pages 8
Publisher ACTA Press
Place of publication Calgary, Alb.
Keyword(s) magnetic resonance imaging
compressive sensing
sparse signals
fourier transform
signal-to noise ratio (SNR)
L I minimization
Summary Magnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.
ISBN 9780889869424
Language eng
DOI 10.2316/P.2013.791-045
Field of Research 080401 Coding and Information Theory
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, ACTA Press
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